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Software ==> | PowerPivot (72 exercises) |
Topic ==> | Calculated columns (7 exercises) |
Level ==> | Average difficulty |
Subject ==> | Power BI training |
This exercise is provided to allow potential course delegates to choose the correct Wise Owl Microsoft training course, and may not be reproduced in whole or in part in any format without the prior written consent of Wise Owl.
The aim of this exercise is to divide staff up into 3 bands as follows:
Year of date of birth | Description |
---|---|
Up to and including 1960 | Old |
Up to and including 1985 | Middle-aged |
Anything else | Young |
You should then use this to get a pivot table showing the average quantity of goods in each transaction by age category, as well as the number of transactions:
There isn't much difference, making one wonder whether this is genuine data, or just randomly generated numbers. Surely Wise Owl wouldn't stoop so low?
To start, if you haven't already done so run the script in the above folder to generate the MAM database (not for commercial use or copying).
Now import the tblStaff, tblPos and tblTransaction tables (giving them friendly names), and create a relationship between the Staff and Pos tables.
In the staff table, create two new calculated columns:
Column name | What it should contain |
---|---|
BirthYear | The year in which this person was born. |
AgeBand | A verdict on the person's age, using the table above. |
Use either a nested IF function to get the age band, or else (better) the SWITCH function.
Use this information to derive the pivot table at the start of this exercise.
Save your workbook as Feeling old, then close it down.
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